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One Pretrained Decision Model Is Enough for Training-Free Generalist Graph Anomaly Detection
Graph anomaly detection (GAD) aims to identify irregular entities in graph-structured data, yet heterogeneous anomaly patterns and distributional differences across graphs challenge generalizable detection. Existing training-free approaches avoid task-specific optimization but primarily rely on pred
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